Soft fault diagnosis of analog circuits based on semi-supervised support vector machine

Abstract

Soft fault diagnosis has been validated as a very challenging problem in analog circuits. In order to improve the generalization ability and close to the practical application of fault diagnosis models, a novel method for obtaining a large number of training samples in soft fault interval is proposed in the present work. Training samples are randomly generated in the interval of soft fault to adapt the continuously change of component parameters. Limits of experimental conditions, lead to the limited number of samples labeled by experts, so that training samples are classified using the semi-supervised support vector machine (S3VM) algorithm. Manifold learning algorithm has been used for the feature extraction and the dimension reduction of soft fault time domain response data in analog circuits. Then the semi-supervised Gaussian mixture model (SGMM) is applied to cluster decision trees. Finally, the S3VM classification is used for the soft fault diagnosis in analog circuits. Experimental results show that the proposed method can be utilized in the single and double soft fault diagnosis of analog circuits. It is observed that the S3VM method is extremely significant as a guide in actual engineering applications, although the diagnosis rate is slightly less than the fixed-parameter offset soft faults.